The current state of the art of in silico drug discovery relies almost exclusively on molecular mechanics force fields, such as AMBER, and empirical potentials. It is well known that while these approaches are excellent for certain applications, they have thus far proven less then satisfactory for thorough understanding the interactions of enzyme-inhibitor systems. To address these issues, our linear scaling, quantum mechanics (QM) algorithm will be applied in the Phase I effort to further research, transfer, and validate a QM-based tool to derive the pairwise energy decomposition (PWD) between a set of targets and a large population of inhibitors. Further, the multifaceted workflow of this process will be fully explored with the Discovery Machine (DM) platform in order to set the groundwork for continued development and to begin to address the ease of use concerns with this level of theory. In the Phase II effort, this PWD technology, along with our proprietary QMScore technology, will be developed as an InteractionProfiler tool and validated against a number of structures by leveraging new industry collaborations. We will also develop the client/server software and database backend necessary to properly exploit these powerful QM tools in an industrial or a government setting. DM will continue to play a significant roll in this process, and the ultimate goal of this fast track SBIR will be an intelligent and adaptive system for QM-based drug discovery with the capability of expanding the user's understanding of the types and strengths of enzyme-inhibitor interactions that play an important roll in the user's in silico drug discovery efforts.